Day 10 – Taking User Input in Python

Welcome to Day 10 of 100 Days of Python!

Until now, most of our programs have worked with values that we directly wrote inside the code.

But what if we want the user to provide the data while the program is running?

That's where Python's built-in input() function comes in.

In this lesson, we will learn:

• How to take input from a user

• How input() works

• Why input() always returns a string

• How to take integer and float input

• How to use a prompt with input()

• How type casting works with user input

• A common mistake when performing calculations with input

1. What is User Input?

User input is data provided by a user while a program is running.

Python provides the built-in input() function to take input from the user.

The basic syntax is:

variable = input()

For example:

name = input()

print(name)

If the user enters:

Sritesh

The output will be:

Sritesh

The value entered by the user is stored in the variable name.

2. How Does input() Work?

When Python encounters input(), the program pauses and waits for the user to enter something.

For example:

name = input()

print("Hello, " + name)

If the user enters:

Sritesh

The program produces:

Hello, Sritesh

So the basic flow is:

Program
   ↓
input()
   ↓
User enters data
   ↓
Python receives the data
   ↓
Value is stored in a variable

3. Important: input() Always Returns a String

This is one of the most important things to remember about input():

The input() function always returns the user's input as a string (str).

For example:

age = input()

print(age)
print(type(age))

If the user enters:

23

The output will be:

23
<class 'str'>

Even though we entered 23, Python received it as:

"23"

not:

23

This becomes very important when we want to perform calculations.

4. Taking Integer Input

If we need an integer from the user, we can use int() with input().

age = int(input())

print(age)
print(type(age))

If the user enters:

23

Output:

23
<class 'int'>

Here:

input()

takes the input as a string, and:

int()

converts that string into an integer.

The process is:

User enters 23
      ↓
input()
      ↓
"23"
      ↓
int()
      ↓
23

5. Taking Float Input

We can also take decimal values using float().

price = float(input())

print(price)
print(type(price))

If the user enters:

99.50

Output:

99.5
<class 'float'>

The general pattern is:

integer_value = int(input())
float_value = float(input())
string_value = input()

6. Displaying a Message with input()

We don't have to leave input() empty.

We can provide a message inside the parentheses.

This message is called the prompt.

Example:

name = input("Enter your name: ")

print(name)

The user will see:

Enter your name: Sritesh

After entering the name, the program prints:

Sritesh

The syntax is:

variable = input("Prompt message")

7. Creating a Greeting Program

We can combine input() with string concatenation to create a simple interactive program.

name = input("Enter your name: ")

print("Hello, " + name + "!")

Example:

Enter your name: Sritesh
Hello, Sritesh!

This is more useful than hard-coding the name:

print("Hello, Sritesh!")

because now the program can greet different users.

8. Taking Multiple Inputs

We can take multiple values from the user and store them in different variables.

For example:

first_name = input("Enter your first name: ")
last_name = input("Enter your last name: ")

print("Hello,", first_name, last_name)

Example:

Enter your first name: Sritesh
Enter your last name: Suranjan
Hello, Sritesh Suranjan

9. A Common Mistake with Numbers

Consider this program:

a = input("Enter first number: ")
b = input("Enter second number: ")

print(a + b)

Suppose the user enters:

10
20

You might expect:

30

But the output is:

1020

Why?

Because input() returns strings.

Python is actually performing:

"10" + "20"

For strings, + means concatenation, not mathematical addition.

So:

"10" + "20"
      ↓
"1020"

10. Converting User Input to Integers

To perform numerical addition, we need to convert the input into integers.

a = input("Enter first number: ")
b = input("Enter second number: ")

print(int(a) + int(b))

If the user enters:

10
20

The output is:

30

We can also convert the values while taking the input:

a = int(input("Enter first number: "))
b = int(input("Enter second number: "))

print(a + b)

This is a very common pattern in Python.

11. Practical Example

Let's combine everything we have learned so far.

name = input("Enter your Name: ")
print("Hello, " + name + "!")

first_number = input("Enter first number: ")
second_number = input("Enter second number: ")

print(first_number + second_number)
print(int(first_number) + int(second_number))

Example:

Enter your Name: Sritesh
Hello, Sritesh!
Enter first number: 10
Enter second number: 20
1020
30

The first result:

1020

is string concatenation.

The second result:

30

is numerical addition after type conversion.

This example connects directly with what we learned on Day 9 – Type Casting in Python.

12. input() with Different Data Types

Here are some common patterns:

String

name = input("Enter your name: ")

Integer

age = int(input("Enter your age: "))

Float

price = float(input("Enter the price: "))

Remember:

input()

itself always produces a string.

The conversion happens because we explicitly use:

int()

or:

float()

13. Complete Program

Here is the complete program for today's lesson:

a = input("Enter your Name: ")
print("Hello, " + a + "!")

b = input("Enter first number: ")
c = input("Enter second number: ")

print(b + c)
print(int(b) + int(c))

Example output:

Enter your Name: Sritesh
Hello, Sritesh!
Enter first number: 10
Enter second number: 20
1020
30

14. Important Things to Remember

input() always returns a string

value = input()
print(type(value))

Output:

<class 'str'>

Use int() for integers

age = int(input())

Use float() for decimal numbers

price = float(input())

Use a prompt to guide the user

name = input("Enter your name: ")

Don't forget type conversion for calculations

a = int(input())
b = int(input())

print(a + b)

15. Quick Revision

                User Input
                    │
                    ▼
                 input()
                    │
                    ▼
              Always returns
                 a string
                    │
          ┌─────────┴─────────┐
          ▼                   ▼
       int()                float()
          │                   │
          ▼                   ▼
       Integer              Float

Remember These:

name = input("Enter your name: ")

age = int(input("Enter your age: "))

price = float(input("Enter the price: "))

And remember the classic beginner example:

"10" + "20"           # "1020"

int("10") + int("20") # 30

16. Key Takeaways

After completing Day 10, you should understand:

• What user input is

• How to use Python's input() function

• How to store user input in variables

• Why input() always returns a string

• How to take integer input using int()

• How to take decimal input using float()

• How to display a prompt to the user

• Why "10" + "20" produces "1020"

• How type casting allows us to perform numerical calculations

• How to create a simple interactive Python program

The input() function is an important step because our programs are no longer limited to values written directly in the source code. We can now make programs that interact with users and respond to the data they provide.

📂 Day 10 Resources

All notes and code for this day are available in the GitHub repository:

https://github.com/SriteshSuranjan/100-Days-of-Python/tree/main/10-Day10-Taking-User-Input

What is Type Casting?

Type casting, also called type conversion, is the process of converting a value from one data type to another.

For example, a value stored as a string can be converted into an integer:

a = "10"
b = int(a)

print(b)
print(type(b))

Output:

10
<class 'int'>

Python provides several built-in functions that can be used for type conversion, including:

Types of Type Conversion

Python type conversion can generally be understood in two ways:

1. Explicit Type Conversion

2. Implicit Type Conversion

1. Explicit Type Conversion

Explicit type conversion happens when the programmer manually converts a value from one data type to another.

This is also commonly called explicit type casting.

Python's built-in conversion functions such as int(), float(), and str() can be used for this purpose.

Example

string = "15"
number = 7

string_number = int(string)

total = number + string_number

print("The sum of both numbers is:", total)

Output:

The sum of both numbers is: 22

Here:

string = "15"

is a string.

We explicitly convert it to an integer using:

string_number = int(string)

Now the addition can be performed between two integers.

Important

The string must contain a valid integer representation.

For example:

int("15")

works, but:

int("hello")

raises a ValueError.

Similarly:

int("15.5")

also raises a ValueError because "15.5" is not a valid integer literal for int().

Common Explicit Conversions

String to Integer

a = "10"
b = int(a)

print(b)
print(type(b))

Output:

10
<class 'int'>

Integer to Float

a = 10
b = float(a)

print(b)
print(type(b))

Output:

10.0
<class 'float'>

Integer to String

a = 100
b = str(a)

print(b)
print(type(b))

Output:

100
<class 'str'>

2. Implicit Type Conversion

Implicit type conversion happens when Python automatically converts a value to another compatible type during an operation.

For example:

a = 7
b = 3.0

c = a + b

print(c)
print(type(c))

Output:

10.0
<class 'float'>

Here:

• a is an int.

• b is a float.

• Python converts the integer value 7 to a floating-point value for the addition.

• The result is a float.

Conceptually:

7 + 3.0
↓
7.0 + 3.0
↓
10.0

This happens automatically, so we do not need to call float() ourselves.

Why Does Python Do This?

Python performs certain automatic conversions when doing so is appropriate and does not require losing information.

For example:

a = 7
b = 3.0

print(a + b)

produces:

10.0

The result is a float because the operation involves a floating-point value.

However, Python does not automatically convert unrelated types in every situation.

For example:

a = "1"
b = 2

print(a + b)

This raises a TypeError.

Python does not automatically convert "1" into 1.

We must explicitly convert it:

a = "1"
b = 2

print(int(a) + b)

Output:

3

Explicit vs Implicit Type Conversion

Important Examples

Example 1: Strings

a = "1"
b = "2"

print(a + b)

Output:

12

Why?

Both values are strings, so + performs string concatenation.

It does not perform numerical addition.

Example 2: Explicit Conversion

a = "1"
b = "2"

print(int(a) + int(b))

Output:

3

Here we explicitly convert both strings into integers.

Example 3: Implicit Conversion

c = 1.9
d = 8

print(c + d)
print(type(c + d))

Output:

9.9
<class 'float'>

Python automatically handles the integer and float combination, producing a float result.

Key Takeaways

1. Type casting means converting a value from one data type to another.

2. Explicit conversion is performed manually by the programmer.

3. Functions such as int(), float(), and str() are commonly used for explicit conversion.

4. Implicit conversion is performed automatically by Python in certain compatible operations.

5. "1" + "2" produces "12" because both values are strings.

6. int("1") + int("2") produces 3.

7. 1 + 2.5 produces 3.5 because the integer participates in a floating-point operation.

8. Python does not automatically convert unrelated types such as str and int during +.

9. Type conversion is important when working with user input, calculations, files, APIs, and data processing.

Quick Revision

Type Casting
│
├── Explicit Conversion
│   ├── int()
│   ├── float()
│   ├── str()
│   ├── list()
│   ├── tuple()
│   └── set()
│
└── Implicit Conversion
    └── Python automatically performs
        certain compatible conversions

Remember

"1" + "2"          # "12"
int("1") + int("2") # 3
1 + 2.5            # 3.5

Type casting becomes especially useful when handling values coming from sources such as input(), files, APIs, databases, and user-entered data.

📂 Day 9 Resources

All notes and code for this day are available in the GitHub repository:

https://github.com/SriteshSuranjan/100-Days-of-Python/tree/main/09-Day09-Typecasting-in-Python

Welcome to Day 8 of 100 Days of Python!

In Day 7, I was given my first Python exercise:

Create a calculator capable of performing addition, subtraction, multiplication, and division on two numbers and display the output in a readable format.

Today, we will look at the solution and understand how the program works.

The calculator uses variables, arithmetic operators, and the print() function.

1. The Calculator

Here is the solution:

a = 7
b = 3

print("Addition of", a, "and", b, "is:", a + b)
print("Subtraction of", a, "and", b, "is:", a - b)
print("Multiplication of", a, "and", b, "is:", a * b)
print("Division of", a, "and", b, "is:", a / b)

print("Floor Division of", a, "and", b, "is:", a // b)
print("Modulus of", a, "and", b, "is:", a % b)
print("Exponentiation of", a, "and", b, "is:", a ** b)

2. Understanding the Variables

First, we create two variables:

a = 7
b = 3

Here:

• a stores the value 7

• b stores the value 3

These two variables are used as the operands for our calculations.

3. Addition

print("Addition of", a, "and", b, "is:", a + b)

The + operator performs addition.

Since:

7 + 3 = 10

The output is:

Addition of 7 and 3 is: 10

4. Subtraction

print("Subtraction of", a, "and", b, "is:", a - b)

The - operator performs subtraction.

7 - 3 = 4

Output:

Subtraction of 7 and 3 is: 4

5. Multiplication

print("Multiplication of", a, "and", b, "is:", a * b)

The * operator performs multiplication.

7 × 3 = 21

Output:

Multiplication of 7 and 3 is: 21

In Python, multiplication is written using *.

6. Division

print("Division of", a, "and", b, "is:", a / b)

The / operator performs regular division.

7 / 3 = 2.3333333333333335

Output:

Division of 7 and 3 is: 2.3333333333333335

Python returns a floating-point value when using /.

7. Floor Division

Although floor division was not one of the four required operations in the original exercise, I also included it for additional practice.

print("Floor Division of", a, "and", b, "is:", a // b)

The // operator performs floor division.

7 // 3 = 2

Output:

Floor Division of 7 and 3 is: 2

8. Modulus

The % operator returns the remainder after division.

print("Modulus of", a, "and", b, "is:", a % b)

Since:

7 ÷ 3 = 2 remainder 1

We get:

7 % 3 = 1

Output:

Modulus of 7 and 3 is: 1

9. Exponentiation

The ** operator performs exponentiation.

print("Exponentiation of", a, "and", b, "is:", a ** b)

This means:

7 ** 3
= 7 × 7 × 7
= 343

Output:

Exponentiation of 7 and 3 is: 343

10. Complete Output

When the complete program is executed, the output will be:

Addition of 7 and 3 is: 10
Subtraction of 7 and 3 is: 4
Multiplication of 7 and 3 is: 21
Division of 7 and 3 is: 2.3333333333333335
Floor Division of 7 and 3 is: 2
Modulus of 7 and 3 is: 1
Exponentiation of 7 and 3 is: 343

11. What We Used

This small calculator combines several concepts learned in the previous days.

Variables

a = 7
b = 3

Variables store the values that we want to use.

Arithmetic Operators

+   Addition
-   Subtraction
*   Multiplication
/   Division
//  Floor Division
%   Modulus
**  Exponentiation

print()

The print() function displays the operation and its result in a readable format.

For example:

print("Addition of", a, "and", b, "is:", a + b)

This combines text, variables, and an arithmetic expression in one statement.

12. Exercise Completed

The original exercise required:

• Addition

• Subtraction

• Multiplication

• Division

• Readable output

The solution successfully performs all four required operations.

I also added:

• Floor Division

• Modulus

• Exponentiation

as additional practice with the arithmetic operators learned in Day 7.

13. Quick Revision

14. Key Takeaways

After completing this exercise, I can now:

• Store numbers in variables

• Perform basic arithmetic operations

• Use Python arithmetic operators

• Combine variables and operators in expressions

• Display calculation results using print()

• Format output so that it is easier to read

• Understand the difference between / and //

• Understand how % returns a remainder

• Use ** for exponentiation

This is a very small program, but it is an important first step toward building larger Python programs.

Day 7 gave me the problem. Day 8 gave me the solution.

The next step is to keep building on these fundamentals and gradually make the programs more interactive and useful.

📂 Day 8 Resources

All notes and code for this day are available in the GitHub repository:

https://github.com/SriteshSuranjan/100-Days-of-Python/tree/main/08-Day8-Exercise-1-Create-a-Calculator-Solution

Welcome to Day 7 of 100 Days of Python!

Today is our first exercise day.

Instead of learning a completely new concept, we will apply what we have learned so far to understand Python operators and build the logic for a simple calculator.

The goal is to practice arithmetic operations and become comfortable with writing expressions in Python.

Note: The complete calculator solution is intentionally not provided today. The solution will be covered in a later day.

1. Python Operators

An operator is a symbol or keyword that tells Python to perform an operation on one or more values.

For example:

5 + 6

Here:

• 5 and 6 are operands

• + is the operator

• The result is 11

Python provides different types of operators for different purposes.

Some common categories include:

• Arithmetic operators

• Comparison operators

• Assignment operators

• Logical operators

• Identity operators

• Membership operators

• Bitwise operators

Today, we are focusing on arithmetic operators.

2. Arithmetic Operators

Arithmetic operators are used to perform mathematical operations.

Let's understand each one.

2.1 Addition – +

The + operator adds two values.

15 + 7

Result:

22

Example:

a = 15
b = 7

print(a + b)

Output:

22

2.2 Subtraction – -

The - operator subtracts the second value from the first.

15 - 7

Result:

8

2.3 Multiplication – *

The * operator multiplies two values.

5 * 7

Result:

35

2.4 Exponentiation – **

The ** operator raises one number to the power of another.

5 ** 3

This means:

5 × 5 × 5

Result:

125

In Python, the correct name is exponentiation.

2.5 Division – /

The / operator performs division.

15 / 7

Result:

2.142857142857143

Python's / operator returns a floating-point result.

For example:

15 / 5

Output:

3.0

Notice that the result is 3.0, not 3.

2.6 Modulus – %

The % operator returns the remainder after division.

15 % 7

7 goes into 15 two times, leaving a remainder of 1.

Therefore:

15 % 7 = 1

Example:

print(15 % 7)

Output:

1

The modulus operator is particularly useful when working with things such as:

• Even and odd numbers

• Cycles

• Remainders

• Divisibility checks

We will use it much more in later exercises.

2.7 Floor Division – //

The // operator performs division and returns the floor of the result.

For positive numbers:

15 // 7

The normal division is approximately:

2.142857...

Floor division gives:

2

Example:

print(15 // 7)

Output:

2

Important: Floor division is not simply "division without decimals" for every possible value. It returns the mathematical floor of the division result, which is especially important when negative numbers are involved.

3. Understanding the Exercise

Exercise 1 – Create a Calculator

Create a calculator capable of performing:

• Addition

• Subtraction

• Multiplication

• Division

The program should work with two numbers and display the results in a readable format.

For example, if the two numbers are:

n = 15
m = 7

You should calculate:

n + m
n - m
n * m
n / m

The output should clearly tell the user which operation was performed.

4. Understanding the Provided Example

A basic example of the required logic is:

n = 15
m = 7

ans1 = n + m
print("Addition of", n, "and", m, "is", ans1)

ans2 = n - m
print("Subtraction of", n, "and", m, "is", ans2)

ans3 = n * m
print("Multiplication of", n, "and", m, "is", ans3)

ans4 = n / m
print("Division of", n, "and", m, "is", ans4)

ans5 = n % m
print("Modulus of", n, "and", m, "is", ans5)

ans6 = n // m
print("Floor Division of", n, "and", m, "is", ans6)

Here:

• n and m are variables containing the two numbers.

• ans1 stores the addition result.

• ans2 stores the subtraction result.

• ans3 stores the multiplication result.

• ans4 stores the division result.

• ans5 stores the modulus result.

• ans6 stores the floor-division result.

This is an example of breaking a calculation into multiple steps.

5. Your Exercise

Now try creating the calculator yourself.

Start with two numbers:

n = 15
m = 7

Then calculate:

Addition
Subtraction
Multiplication
Division

Try to format the output so that it is easy to understand.

Challenge

Before looking at a solution, try answering these questions:

1. Which operator is used for addition?

2. Which operator is used for multiplication?

3. What is the difference between / and //?

4. What does % return?

5. What does ** do?

6. Can you store each result in a separate variable?

7. Can you print the result in a readable sentence?

6. Operator Practice

The following expressions are included in today's practice program:

print(5 + 6)
print(15 - 6)
print(15 * 6)
print(15 / 6)
print(15 // 6)
print(5 % 3)
print(2 ** 4)

Their results are:

11
9
90
2.5
2
2
16

Try predicting the output before running the program.

This is a simple but effective way to improve your understanding of operators.

7. Quick Revision

Remember:

/   → Division
//  → Floor Division
%   → Remainder
**  → Power

8. What I Learned Today

Day 7 was mainly about practice rather than new theory.

Today I learned:

• What operators are

• What arithmetic operators are

• How to perform addition

• How to perform subtraction

• How to perform multiplication

• How to perform division

• How to calculate a remainder using %

• How to perform floor division using //

• How to calculate powers using **

• How arithmetic expressions can be used to build the logic of a calculator

Most importantly, I practiced applying variables, print(), and arithmetic operators together.

9. Exercise Status

Exercise: Create a Calculator

Status: 🟡 Attempted / Practice

The complete solution is intentionally not included in this day's code.

I will revisit this exercise in a later day and build upon what I have learned.

Final Note

A calculator may look like a very small program, but it introduces an important programming habit:

Learn a concept → practice it → build something with it.

That is the approach I want to follow throughout these 100 Days of Python.

📂 Day 7 Resources

All notes and code for this day are available in the GitHub repository:

https://github.com/SriteshSuranjan/100-Days-of-Python/tree/main/07-Day07-Exercise-1-Create-a-Calculator

Welcome to Day 6 of 100 Days of Python!

Today, we are learning two of the most fundamental concepts in programming:

• Variables

• Data Types

Whenever we write a Python program, we work with different kinds of values such as numbers, text, True/False, collections, and more. Variables give these values names, while data types tell Python what kind of values they are.

1. What is a Variable?

A variable is a name that refers to a value or object in a Python program.

You can think of a variable as a label attached to some data.

For example:

a = 1
b = True
c = "Sritesh"
d = None

Here:

• a refers to the integer 1

• b refers to the Boolean value True

• c refers to the string "Sritesh"

• d refers to None

Python variables do not need their data type to be declared explicitly.

For example:

age = 23
name = "Sritesh"
height = 5.5

Python automatically determines the type of each value.

2. Creating Variables

Creating a variable in Python is simple.

The general syntax is:

variable_name = value

Example:

name = "Sritesh"
age = 23
is_learning = True

The = symbol is called the assignment operator.

It assigns the value on the right to the variable name on the left.

For example:

age = 23

means that age now refers to the value 23.

3. What is a Data Type?

A data type describes the kind of value an object represents.

Different types of data support different operations.

For example:

a = 10
b = 20

print(a + b)

Output:

30

Here, a and b are integers, so addition performs numerical addition.

Now consider:

a = "10"
b = "20"

print(a + b)

Output:

1020

Here, a and b are strings, so + joins the strings together.

This is why understanding data types is important.

4. Checking the Type of a Value

Python provides the built-in type() function to check the type of an object.

Example:

a = 10
print(type(a))

Output:

<class 'int'>

Another example:

name = "Sritesh"
print(type(name))

Output:

<class 'str'>

The type() function is extremely useful while learning Python and debugging programs.

5. Common Built-in Data Types

Python provides several built-in data types.

Some important ones are:

In this lesson, we will focus on the types introduced in this day's program.

6. Numeric Data Types

Python has three built-in numeric types:

• int

• float

• complex

int

int represents whole numbers.

Examples:

a = 10
b = -8
c = 0

These are all integers.

You can perform mathematical operations on integers:

a = 10
b = 5

print(a + b)
print(a - b)
print(a * b)

float

float represents numbers containing a decimal point.

Examples:

a = 7.349
b = -9.0
c = 0.0000001

You can check its type:

a = 7.5
print(type(a))

Output:

<class 'float'>

complex

Python also supports complex numbers.

A complex number contains:

• A real part

• An imaginary part

Python uses j to represent the imaginary part.

For example:

a = 1 + 2j

or:

a = complex(1, 2)

Both represent the same complex number.

print(a)

Output:

(1+2j)

Note: In Python, use j, not i, for the imaginary part.

7. Text Data – str

The str type is used to represent text.

Strings can be written using single or double quotes.

name = "Sritesh"
language = 'Python'

You can also store sentences:

message = "I am learning Python."

Check the type:

print(type(message))

Output:

<class 'str'>

8. Boolean Data Type – bool

The Boolean data type has only two possible values:

True
False

Example:

is_learning = True
is_sleeping = False

Booleans are commonly used when making decisions in programs.

For example:

age = 23
can_vote = age >= 18

print(can_vote)

Output:

True

Remember: True and False must begin with a capital letter.

9. NoneType – None

Python has a special value called None.

It represents the absence of a value or a value that is currently not available.

Example:

result = None

print(result)
print(type(result))

Output:

None
<class 'NoneType'>

None is different from:

• 0

• False

• ""

It specifically represents the absence of a value.

10. Sequence Data Types

Python provides several sequence types.

In this lesson, we will look at:

• list

• tuple

List

A list is an ordered, mutable collection of items.

Lists are written using square brackets [].

Example:

list1 = [8, 2.3, [-4, 5], ["apple", "banana"]]

print(list1)

Output:

[8, 2.3, [-4, 5], ['apple', 'banana']]

A list can contain different types of values, including other lists.

For example:

numbers = [10, 20, 30]
names = ["Sritesh", "Python"]
mixed = [10, "Python", True, 5.5]

Lists are mutable, which means their contents can be changed after creation.

For example:

numbers = [10, 20, 30]

numbers[0] = 100

print(numbers)

Output:

[100, 20, 30]

We will explore lists in much more detail in a later day.

11. Tuple

A tuple is an ordered, immutable collection of items.

Tuples are commonly written using parentheses ().

Example:

tuple1 = (("parrot", "sparrow"), ("Lion", "Tiger"))

print(tuple1)

Output:

(('parrot', 'sparrow'), ('Lion', 'Tiger'))

Unlike lists, tuples are immutable.

That means their existing elements cannot be changed after the tuple is created.

Example:

numbers = (10, 20, 30)

You cannot directly change one of its elements like you can with a list.

We will learn more about tuples later.

12. Dictionary – dict

A dictionary stores data as key-value pairs.

Dictionaries are written using curly brackets {}.

Example:

student = {
    "name": "Sakshi",
    "age": 20,
    "canVote": True
}

print(student)

Output:

{'name': 'Sakshi', 'age': 20, 'canVote': True}

Here:

• "name" is a key and "Sakshi" is its value

• "age" is a key and 20 is its value

• "canVote" is a key and True is its value

A dictionary allows us to store related information using meaningful keys.

For example:

person = {
    "name": "Sritesh",
    "age": 23,
    "language": "Python"
}

We can later access values using their keys.

print(person["name"])

Output:

Sritesh

Modern Python dictionaries preserve insertion order, meaning items generally appear in the order they were added. The important concept here is that dictionaries are mappings of keys to values, rather than sequences indexed by position.

13. Checking Data Types

Let's check the types of the different values used in this lesson:

a = complex(1, 2)
b = True
c = "Sritesh"
d = None

print(type(a))
print(type(b))
print(type(c))
print(type(d))

Output:

<class 'complex'>
<class 'bool'>
<class 'str'>
<class 'NoneType'>

We can also check collections:

list1 = [1, 2, 3]
tuple1 = (1, 2, 3)
dict1 = {"name": "Sritesh"}

print(type(list1))
print(type(tuple1))
print(type(dict1))

Output:

<class 'list'>
<class 'tuple'>
<class 'dict'>

14. Putting Everything Together

Here is the complete program from today's lesson:

# Integer and float examples
# a = 123
# a = 1.23

# Complex number
a = complex(1, 2)

# Boolean
b = True

# String
c = "Sritesh"

# None
d = None

print(a)

a1 = 9
print(a + a1)

print("The type of a is", type(a))
print("The type of b is", type(b))
print("The type of c is", type(c))
print("The type of d is", type(d))

# List
list1 = [8, 2.3, [-4, 5], ["apple", "banana"]]

print(list1)
print("The type of list1 is", type(list1))

# Tuple
tuple1 = (("parrot", "sparrow"), ("Lion", "Tiger"))

print(tuple1)
print("The type of tuple1 is", type(tuple1))

# Dictionary
dict1 = {
    "name": "Sakshi",
    "age": 20,
    "canVote": True
}

print(dict1)
print("The type of dict1 is", type(dict1))

15. Quick Revision

Variable

A name that refers to a value or object.

age = 23

int

Whole numbers.

age = 23

float

Decimal numbers.

height = 5.5

complex

Complex numbers.

number = 1 + 2j

str

Text.

name = "Sritesh"

bool

True or False.

is_learning = True

None

Represents the absence of a value.

result = None

list

Ordered and mutable collection.

numbers = [1, 2, 3]

tuple

Ordered and immutable collection.

numbers = (1, 2, 3)

dict

Key-value mapping.

student = {"name": "Sritesh", "age": 23}

16. Key Takeaways

After completing Day 6, you should understand:

• What a variable is

• How to create and assign variables

• What data types are

• How to use the type() function

• int, float, and complex

• str for text

• bool for Boolean values

• None and NoneType

• Lists and their mutability

• Tuples and their immutability

• Dictionaries and key-value pairs

• Why understanding data types is important when writing Python programs

These concepts are the foundation for the next stages of Python. Once we understand how Python represents and stores different kinds of data, we can start performing operations on that data and building more useful programs.

📂 Day 6 Resources

All notes and code for this day are available in the GitHub repository:

https://github.com/SriteshSuranjan/100-Days-of-Python/tree/main/06-Day06-Variables-and-Datatypes

Welcome to Day 5 of 100 Days of Python!

Today, we will learn three useful concepts:

• Python comments

• Escape sequences

• More options of the print() function

These concepts may look simple, but they are important for writing readable Python programs and controlling how output is displayed.

1. Python Comments

A comment is text written in a Python program that is ignored by the Python interpreter during execution.

Comments are mainly used to:

• Explain what a piece of code does

• Make code easier to understand

• Add notes for yourself or other developers

• Temporarily prevent a line of code from executing while testing

Single-Line Comments

In Python, a single-line comment starts with the # symbol.

Everything after # on that line is treated as a comment.

Example 1

# This is a single-line comment

print("This is a print statement.")

Output

This is a print statement.

The comment does not produce any output.

Example 2: Comment After Code

A comment can also be written after a statement.

print("Hello World!")  # Printing Hello World

Output

Hello World!

The Python interpreter executes the print() statement but ignores the comment.

Example 3: Temporarily Disabling Code

Comments can also be useful when testing code.

print("Python Program")

# print("Python Program")

Output

Python Program

The second print() statement does not execute because it has been commented out.

2. Multi-Line Comments

Python does not have a special syntax specifically designed for multi-line comments.

If you want to write a comment across multiple lines, the recommended approach is to use # on each line.

Example

# This program checks whether p is greater than 5.
# If the condition is true, the first message is printed.
# Otherwise, the second message is printed.

p = 7

if p > 5:
    print("p is greater than 5.")
else:
    print("p is not greater than 5.")

Output

p is greater than 5.

Triple-Quoted Strings

You may also see triple quotes being used for blocks of text:

"""
This looks like a multi-line comment.
It is actually a multi-line string.
"""

p = 7

if p > 5:
    print("p is greater than 5.")
else:
    print("p is not greater than 5.")

Output

p is greater than 5.

Triple-quoted text is technically a string literal, not a comment.

Triple-quoted strings are commonly used for docstrings, which document functions, classes, and modules.

For example:

def greet():
    """This function prints a greeting."""
    print("Hello!")

For normal comments, prefer #.

3. Escape Sequences

An escape sequence is a special combination of characters used inside a string to represent characters or actions that would otherwise be difficult to write directly.

Escape sequences usually begin with a backslash (\).

For example, suppose we want to include double quotes inside a string that is already surrounded by double quotes.

This would cause a problem:

print("This doesn't "execute" correctly")

Python cannot determine where the string ends.

We can use the \" escape sequence:

print("This will \"execute\" correctly")

Output

This will "execute" correctly

Common Escape Sequences

Here are some useful escape sequences:

Example

print("Hello\nPython")

Output:

Hello
Python

The \n moves the output to a new line.

Another example:

print("Name:\tSritesh")

Output:

Name:   Sritesh

The \t inserts a tab space.

4. More About the print() Function

So far, we have used print() to display text and numbers.

Python's print() function provides additional parameters that allow us to control how multiple values are displayed.

A simplified form of its syntax is:

print(*objects, sep=' ', end='\n', file=None, flush=False)

The most commonly useful parameters for beginners are:

• objects

• sep

• end

objects

print() can display one or more objects.

print("Hello")
print(10)
print("Python", 100)

Output:

Hello
10
Python 100

When multiple objects are passed to print(), Python separates them with a space by default.

5. The sep Parameter

sep stands for separator.

It controls what is placed between multiple objects.

The default value is a space:

print("Python", "Java", "C++")

Output:

Python Java C++

We can change the separator:

print("Python", "Java", "C++", sep="~")

Output:

Python~Java~C++

Another example:

print("2026", "09", "12", sep="-")

Output:

2026-09-12

6. The end Parameter

By default, print() moves to a new line after displaying the output.

This happens because the default value of end is "\n".

For example:

print("Hello")
print("World")

Output:

Hello
World

We can change the ending character.

print("Hello", end=" ")
print("World")

Output:

Hello World

We can also use another string:

print("Hello", end="003\n")
print("World")

Output:

Hello003
World

7. Combining sep and end

The sep and end parameters can also be used together.

print("Hey", 6, 7, sep="~", end="003\n")

Output:

Hey~6~7003

Here:

• sep="~" places ~ between Hey, 6, and 7

• end="003\n" adds 003 after the final value and then moves to a new line

8. Putting Everything Together

Here is a small program using comments, escape sequences, sep, and end:

# Learning comments, escape sequences and print()

print("Hey! I am a \"Good Boy\".\nI am learning Python")

print("Hey", 6, 7, sep="~", end="003\n")

print("Hello!")

Output

Hey! I am a "Good Boy".
I am learning Python
Hey~6~7003
Hello!

9. Common Mistakes

Mistake 1: Forgetting the #

Incorrect:

This is a comment

Python will try to interpret it as code.

Correct:

# This is a comment

Mistake 2: Incorrectly using quotes inside a string

Incorrect:

print("He said "Hello"")

Correct:

print("He said \"Hello\"")

Or use different quote types:

print('He said "Hello"')

Mistake 3: Confusing sep and end

Remember:

• sep → controls the separator between objects

• end → controls what is printed after the final object

10. Quick Revision

Comments

# This is a comment

Comments are ignored by Python and are used to explain code or temporarily disable code.

Escape Sequences

Escape sequences begin with \.

Common examples:

\n  → New line
\t  → Tab
\\  → Backslash
\"  → Double quote
\'  → Single quote

print()

Basic usage:

print("Hello")

Using sep:

print("A", "B", "C", sep="-")

Output:

A-B-C

Using end:

print("Hello", end=" ")
print("World")

Output:

Hello World

11. Key Takeaways

After Day 5, you should understand:

• What comments are and why they are useful

• How to create single-line comments using #

• Why triple-quoted text is technically a string rather than a comment

• What escape sequences are

• How \n, \t, \\, \", and \' work

• How print() accepts multiple objects

• How sep changes the separator between objects

• How end changes what print() outputs after each call

These are small concepts, but they will appear frequently in Python programs as we move into variables, data types, conditions, loops, functions, and beyond.

📂 Day 5 Resources

All notes and code for this day are available in the GitHub repository:

https://github.com/SriteshSuranjan/100-Days-of-Python/tree/main/05-Day05-Comments-and-Print

Welcome to Day 4 of 100 Days of Python!

So far, we have learned:

• What programming is

• What Python is

• Where Python is used

• What modules and packages are

• How pip is used to install Python packages

Today, we are finally going to write our first Python program from scratch.

It may look very simple—and it is.

But every Python developer has to start somewhere.

Today, that starting point is the print() function.

The print() Function

The print() function is used to display information on the console.

The simplest example is:

print("Hello World!")

Output:

Hello World!

Here:

• print is a built-in Python function.

• "Hello World!" is a string.

• The value inside the parentheses is passed to print().

• Python displays the result on the console.

Printing Text

We can use print() to display text.

Text in Python is written inside quotes.

For example:

print("Hello World!")
print("Welcome to Python!")
print("I am learning Python.")

Output:

Hello World!
Welcome to Python!
I am learning Python.

Each print() statement normally displays its output on a new line.

Printing Numbers

We can also print numbers directly.

For example:

print(753)

Output:

753

Notice that we don't need quotation marks around a number.

Compare:

print(753)

with:

print("753")

Both display:

753

However, they represent different types of values.

• 753 is a number.

• "753" is text (a string).

We will learn more about data types later.

Printing Multiple Values

The print() function can accept multiple values separated by commas.

For example:

print("Hello World!", 753)

Output:

Hello World! 753

Python separates the values with a space by default.

We can also print several values:

print("Python", "is", "awesome")

Output:

Python is awesome

Printing Calculations

Python can also perform calculations inside print().

For example:

print(753 * 3510)

Python evaluates the multiplication first and then prints the result.

Output:

2643030

This is one of the reasons programming languages are useful: we can give the computer an expression, and it can perform the calculation for us.

Other examples:

print(10 + 5)
print(20 - 8)
print(6 * 7)
print(20 / 4)

Output:

15
12
42
5.0

We will learn operators and calculations in more detail later.

Our First Python Program

Let's combine everything we learned today.

print("Hello World!", 753)
print(753)
print("Bye")
print(753 * 3510)

Output:

Hello World! 753
753
Bye
2643030

This is a very small program, but it already demonstrates several important ideas:

1. Printing text

2. Printing numbers

3. Printing multiple values

4. Performing a calculation

5. Executing multiple statements in sequence

How Python Executes the Program

Python executes our program from top to bottom.

Consider:

print("First")
print("Second")
print("Third")

The output will be:

First
Second
Third

Python starts with the first statement, executes it, moves to the next statement, and continues until the program ends.

This sequential execution will become extremely important when we start learning conditions, loops, functions, and more advanced concepts.

A Small Challenge

Now it is your turn!

Write a Python program that prints a short poem.

For example, the structure could be:

print("Your poem goes here")
print("Another line of your poem")
print("Another line of your poem")

You can choose any poem you like.

The purpose of this exercise is not to write complicated code.

The purpose is to practice writing and executing Python code yourself.

Challenge: Create Your Own Output

Try creating a small program that prints:

• Your name

• A programming-related message

• A number

• A calculation

For example:

print("My name is Sritesh.")
print("I am learning Python.")
print(100)
print(25 * 4)

Try changing the values and see what happens.

Common Mistakes

1. Forgetting Quotes Around Text

Incorrect:

print(Hello World!)

Correct:

print("Hello World!")

Text should generally be enclosed in quotation marks.

2. Missing Parentheses

Incorrect:

print "Hello World!"

Correct:

print("Hello World!")

Python 3 uses parentheses with the print() function.

3. Incorrect String Quotes

Incorrect:

print("Hello World!)

The opening and closing quotes must match.

Correct:

print("Hello World!")

4. Writing a Calculation as Text

Compare:

print(10 + 5)

Output:

15

with:

print("10 + 5")

Output:

10 + 5

The first performs a calculation.

The second simply prints the characters as text.

Quick Revision

What is print()?

print() is a built-in Python function used to display information on the console.

Print text

print("Hello World!")

Print a number

print(753)

Print multiple values

print("Hello", 753)

Print a calculation

print(753 * 3510)

Python execution order

Python normally executes statements from top to bottom.

Day 4 Takeaways

Today we learned:

1. How to write a basic Python program.

2. How to use the print() function.

3. How to print text.

4. How to print numbers.

5. How to print multiple values.

6. How to perform calculations inside print().

7. How Python executes statements sequentially.

8. How to create a simple Python program independently.

Final Thought

Our first program is intentionally simple.

That's okay.

Every large Python application—from automation scripts to Machine Learning systems—is ultimately built from smaller programming concepts.

Today we wrote a few print() statements.

Soon, we will learn how to make our programs store information, make decisions, repeat tasks, accept input, work with data, and solve real problems.

For now:

We have officially written our first Python program.

Day 4 complete. 🚀

📂 Day 4 Resources

All notes and code for this day are available in the GitHub repository:

https://github.com/SriteshSuranjan/100-Days-of-Python/tree/main/04-Day04-Our-First-Program

Welcome to Day 3 of 100 Days of Python!

On Day 1, we learned what Python is and where it is used.

On Day 2, we explored the different kinds of applications that can be built with Python.

Today, we are going to learn two concepts that we will use throughout our Python journey:

• Modules

• pip

As we start building larger Python programs, we don't want to write every piece of functionality from scratch.

Python allows us to reuse existing code through modules and packages.

What is a Module?

A module is a Python file containing code that can be reused in another Python program.

A module can contain:

• Variables

• Functions

• Classes

• Constants

• Other Python code

Instead of writing the same functionality repeatedly, we can put reusable code into a module and import it whenever we need it.

For example, imagine we have a file called:

calculator.py

It could contain:

def add(a, b):
    return a + b

We could then import it into another Python program:

import calculator

result = calculator.add(10, 20)
print(result)

Output:

30

This is one of the fundamental ideas behind code reuse in Python.

Types of Modules

Python modules can broadly be divided into two categories:

1. Standard Library Modules

2. Third-Party Modules

1. Standard Library Modules

Python comes with a large collection of modules as part of its standard library.

These modules are available with a normal Python installation, so we generally do not need to install them separately using pip.

For example:

import hashlib

hashlib is part of Python's standard library and provides common hashing algorithms.

Other examples include:

import math
import random
import os
import sys
import datetime

These modules provide functionality that we can reuse in our programs.

2. Third-Party Modules

Third-party modules/packages are created and maintained outside the Python standard library.

We can install them when we need additional functionality.

For example:

• Pandas → Data analysis

• NumPy → Numerical computing

• Requests → HTTP requests

• Flask → Web applications

• FastAPI → APIs

• OpenCV → Computer vision

• Scikit-learn → Machine Learning

These packages greatly expand what Python can do.

What is pip?

pip is the standard package installer for Python.

It allows us to install Python packages from the Python Package Index (PyPI) and other package sources.

For example, if we want to install Pandas, we can run:

pip install pandas

After installation, we can import it into our Python program:

import pandas

Installing a Package with pip

Let's install Pandas.

Open a terminal or command prompt and run:

pip install pandas

pip will download Pandas and its required dependencies and install them into the appropriate Python environment.

After installation, we can use it in our Python program.

For example:

import pandas

df = pandas.read_csv("words.csv")

print(df)

Here:

• import pandas imports the Pandas package.

• pandas.read_csv() reads a CSV file.

• df stores the resulting data structure.

We will learn Pandas properly later in the Python journey.

import in Python

The import statement allows us to use code from another module or package.

For example:

import math

print(math.sqrt(25))

Output:

5.0

Here:

import math

imports the math module.

We can then access its functionality using:

math.sqrt()

Importing Specific Items

We can also import a specific function or object from a module.

For example:

from math import sqrt

print(sqrt(25))

Output:

5.0

Instead of writing:

math.sqrt(25)

we can directly write:

sqrt(25)

Importing with an Alias

Sometimes module names are long or we simply want a shorter name.

Python allows us to create an alias using the as keyword.

For example:

import pandas as pd

Now we can use:

pd.read_csv("words.csv")

instead of:

pandas.read_csv("words.csv")

You will see this frequently in real-world Python code.

For example:

import numpy as np
import pandas as pd

These are common conventions in the Python ecosystem.

Standard Library vs Third-Party Packages

It is important to understand the difference.

The standard library comes with Python, while third-party packages are installed separately when required.

Package vs Module

These terms are often used together, but they are not exactly the same.

Module

A module is generally a single Python file containing reusable code.

Example:

calculator.py

Package

A package is a way of organizing multiple Python modules into a larger reusable structure.

For example:

my_package/
    module1.py
    module2.py
    module3.py

Packages allow larger projects and libraries to organize their code into logical components.

Why Are Modules and Packages Important?

Imagine building a large application completely from scratch.

You would have to write everything yourself:

• Mathematical functions

• File handling

• HTTP communication

• Data processing

• Database interaction

• Machine Learning algorithms

• Image processing

That would take an enormous amount of time.

Instead, Python developers reuse existing, tested functionality whenever appropriate.

For example:

Python
  │
  ├── Standard Library
  │
  ├── Third-Party Packages
  │       │
  │       ├── NumPy
  │       ├── Pandas
  │       ├── Requests
  │       ├── OpenCV
  │       └── Scikit-learn
  │
  └── Our Own Modules

This ecosystem is one of Python's biggest strengths.

Checking Installed Packages

We can use pip to see packages installed in an environment.

pip list

This displays installed Python packages and their versions.

We can also check information about a particular package:

pip show pandas

Installing a Specific Version

Sometimes a project requires a particular package version.

We can specify the version while installing:

pip install pandas==2.3.2

The exact version should depend on the requirements of the project.

We can also upgrade a package:

pip install --upgrade pandas

Removing a Package

If we no longer need a package, we can uninstall it:

pip uninstall pandas

pip will ask for confirmation before removing the package.

A Note About Virtual Environments

As Python projects become larger, installing every package globally can cause dependency conflicts.

For example:

Project A → requires Package X version 1
Project B → requires Package X version 2

A useful solution is to create a virtual environment for each project.

We will explore virtual environments and dependency management in more detail later.

For now, remember:

A virtual environment provides an isolated Python environment for a project and its dependencies.

Our Day 3 Code

Our basic demonstration contains both a third-party package and a standard-library module:

import pandas
import hashlib

print("Hi!")

Here:

import pandas

imports the third-party Pandas package.

And:

import hashlib

imports a module from Python's standard library.

The important point is that pandas normally needs to be installed separately, while hashlib is available as part of Python's standard library.

Important Commands

Here are the basic pip commands introduced today:

pip install package_name

Install a package.

pip uninstall package_name

Uninstall a package.

pip list

List installed packages.

pip show package_name

Show information about a package.

pip install --upgrade package_name

Upgrade a package.

pip install package_name==version

Install a specific package version.

Common Mistakes

Mistake 1: Forgetting to Install a Third-Party Package

If you write:

import pandas

without having Pandas installed in the active environment, Python may produce:

ModuleNotFoundError

Install it with:

pip install pandas

Mistake 2: Installing Packages in the Wrong Environment

You may install a package successfully but still receive:

ModuleNotFoundError

This can happen when pip installs the package into a different Python environment than the one running your program.

Virtual environments help prevent these problems.

Mistake 3: Confusing pip with import

Remember:

pip install pandas

is a terminal command used to install a package.

Whereas:

import pandas

is Python code used to import the package into your program.

They perform different jobs.

Quick Revision

Module

A reusable Python file containing code such as functions, classes, or variables.

Standard Library

Modules that are distributed with Python.

Examples:

math
random
os
sys
hashlib

Third-Party Package

Software developed outside Python's standard library and generally installed separately.

Examples:

pandas
numpy
requests
flask
opencv-python

pip

Python's standard package installer, commonly used to install and manage Python packages.

Import

Used to make a module or package available in our Python program.

import math

Alias

A different name given to an imported module.

import pandas as pd

Day 3 Takeaways

1. Modules allow us to reuse Python code.

2. Python provides a large standard library.

3. Third-party packages extend Python's capabilities.

4. pip is commonly used to install Python packages.

5. import is used to access modules and packages in Python code.

6. from ... import ... can import specific items.

7. as can create an alias for an import.

8. Virtual environments help isolate project dependencies.

9. Python's package ecosystem is a major reason for its popularity.

Final Thought

One of the most powerful ideas in programming is:

Don't reinvent the wheel when reliable code already exists.

Instead of writing everything from scratch, Python allows us to build on top of a huge ecosystem of existing modules and packages.

Today we learned how to access that ecosystem.

Soon, we will start writing more of our own reusable code as well.

Day 3 complete.

📂 Day 3 Resources

👉 All notes and code for this day are available in the GitHub repository:

https://github.com/SriteshSuranjan/100-Days-of-Python/tree/main/03-Day03-Modules-and-Pip

Welcome to Day 2 of 100 Days of Python!

On Day 1, we learned what programming is, what Python is, its major features, and where Python is used.

Today, instead of learning a large amount of syntax, let's answer a more interesting question:

What can we actually build with Python?

Learning a programming language becomes much more exciting when we understand what it can be used to create.

Python is not limited to simple programs such as printing messages, performing calculations, or writing small scripts. It is used in automation, web development, data analysis, Artificial Intelligence, Machine Learning, computer vision, games, and many other areas.

During this 100 Days of Python journey, we will gradually develop the skills required to build projects like these.

Why Learn Python?

One of the biggest advantages of Python is its versatility.

The same programming language can be used for completely different types of projects.

For example, Python can be used to:

• Automate repetitive tasks

• Build web applications

• Work with APIs

• Analyze data

• Create visualizations

• Build Machine Learning models

• Work with Artificial Intelligence

• Process images and videos

• Create games

• Build command-line tools

• Interact with databases

• Create scripts for system administration

• Build backend services

This means that learning Python gives us a foundation that can be applied to many different areas of technology.

What Can Python Do?

Let's look at some examples.

1. Virtual Assistants

Python can be used to create a virtual assistant that can respond to commands and perform predefined tasks.

For example, a virtual assistant could:

• Take voice commands

• Search for information

• Open applications

• Tell the current time

• Play music

• Automate simple tasks

In this repository, one of the projects is:

Jarvis Virtual Assistant

The project demonstrates how Python can be used to create an interactive application rather than just a simple script.

We will learn the concepts required for projects like this gradually.

2. Automation

Automation is one of the most practical uses of Python.

Imagine having to perform the same task hundreds of times manually.

Instead of doing it manually, Python can perform the task automatically.

For example, Python can be used to:

• Rename hundreds of files

• Organize files into folders

• Generate reports

• Process text files

• Download data

• Send automated emails

• Interact with APIs

• Execute system commands

• Automate repetitive workflows

This is particularly useful in software engineering, DevOps, data processing, and system administration.

3. Web Scraping

Python can also be used to collect information from websites through web scraping, where permitted by the website's terms and technical restrictions.

For example, a Python program can extract structured information from web pages and process it automatically.

Common tools include:

• Beautiful Soup

• Requests

• Selenium

• Scrapy

Web scraping can be useful for:

• Research

• Data collection

• Price monitoring

• Information aggregation

• Testing

• Internal automation

The important thing is to use scraping responsibly and respect the website's terms, robots rules, rate limits, and applicable laws.

4. Web Development

Python can be used to build the backend of web applications.

Popular Python frameworks include:

• Django

• Flask

• FastAPI

These frameworks can be used to build:

• Websites

• REST APIs

• Backend services

• Authentication systems

• Database-driven applications

For example, Flask is a lightweight Python web framework that can be used to create web applications and APIs.

5. Face Recognition and Computer Vision

Python is heavily used in Computer Vision, a field that allows computers to process and understand images and videos.

Popular tools include:

• OpenCV

• NumPy

• MediaPipe

• PyTorch

• TensorFlow

In this repository, we have a:

Face Recognition

project.

This gives us an idea of how Python can move beyond text-based programs and interact with visual data.

Later in our learning journey, we can explore the concepts behind these applications in much greater detail.

6. Games

Python can also be used to create games.

A popular library for this is:

Pygame

In this repository, we have two game projects:

• Flappy Bird Game

• Snake Game

Games are a fun way to understand programming concepts such as:

• Variables

• Conditions

• Loops

• Functions

• Events

• Coordinates

• Collision detection

• Game state

• Object-oriented programming

These projects can turn programming concepts into something visual and interactive.

7. Data Analysis

Python is one of the most widely used languages for working with data.

Python can be used to:

• Load datasets

• Clean data

• Transform data

• Analyze patterns

• Calculate statistics

• Generate reports

• Create visualizations

Popular libraries include:

• NumPy

• Pandas

• Matplotlib

• Seaborn

• Plotly

For example, a company could use Python to analyze sales data and identify trends.

8. Artificial Intelligence and Machine Learning

Python is extremely popular in Artificial Intelligence (AI) and Machine Learning (ML).

Machine Learning allows computers to learn patterns from data and make predictions or decisions.

Python provides a huge ecosystem for this field.

Some important tools include:

• Scikit-learn

• TensorFlow

• PyTorch

• Hugging Face Transformers

Later in our learning journey, Python will become the foundation for exploring:

Python → NumPy → Pandas → Machine Learning → Deep Learning → Generative AI → RAG → AI Agents

So the fundamentals we learn now will become important later.

9. DevOps and Cloud Automation

Python is also useful in DevOps and Cloud Engineering.

Python scripts can be used to automate infrastructure and operational tasks.

For example:

• Automating AWS operations

• Working with cloud APIs

• Processing logs

• Monitoring systems

• Automating deployments

• Managing files and servers

• Creating operational scripts

Python can work alongside technologies such as:

• AWS

• Docker

• Kubernetes

• Terraform

• Jenkins

• Ansible

This makes Python a valuable automation language for engineers as well.

Projects in This Repository

To make this learning journey practical, our repository will gradually contain different Python projects.

Current Day 2 examples include:

1. Jarvis Virtual Assistant

An interactive Python-based virtual assistant.

2. Love Calculator

A simple project demonstrating how programming can be used to create an interactive application.

3. Face Recognition

An introduction to applying Python to computer vision.

4. Flappy Bird Game

A Python game project demonstrating interactive programming.

5. Snake Game

Another game project that can help us understand programming logic and game mechanics.

These projects are examples of what Python can eventually help us build.

We should not worry about understanding every line of these projects immediately.

Don't Try to Learn Everything at Once

If you are a beginner, seeing projects such as AI applications, games, or virtual assistants can feel overwhelming.

Don't worry.

We are not going to jump directly into advanced projects.

Instead, we will build our knowledge step by step.

For example:

Python Fundamentals
        ↓
Variables & Data Types
        ↓
Operators
        ↓
Conditions
        ↓
Loops
        ↓
Functions
        ↓
Data Structures
        ↓
Object-Oriented Programming
        ↓
Modules & Packages
        ↓
Libraries & Frameworks
        ↓
Projects
        ↓
Advanced Python
        ↓
AI / ML / Automation / Backend / Data

Every advanced project is built from smaller programming concepts.

What I Want to Achieve with These 100 Days

The purpose of this challenge is not simply to complete 100 files.

The goal is to actually understand Python and develop the ability to use it to solve problems.

Throughout these 100 days, I want to:

• Understand Python fundamentals

• Write Python programs independently

• Improve problem-solving skills

• Build practical projects

• Learn important Python libraries

• Understand how Python is used in real-world technology

• Create a permanent reference of my learning

• Build a foundation for future AI/ML and automation work

The code and notes in this repository will document that progress.

Day 2 Takeaways

Python is a general-purpose language that can be used for many different types of applications.

Some major areas where Python is used are:

Final Thought

Learning Python is not about memorizing hundreds of commands.

It is about learning how to think, solve problems, and turn those solutions into working programs.

Today we only looked at what Python can do.

As we progress through these 100 days, we will gradually learn the concepts required to build these kinds of applications ourselves.

Day 2 complete.

Next: Let's start learning the actual building blocks of Python.

📂 Day 2 Resources

👉 All notes and code for this day are available in the GitHub repository:

https://github.com/SriteshSuranjan/100-Days-of-Python/tree/main/02-Day02-Applications-of-Python

Welcome to Day 1 of 100 Days of Python!

In this series, we will start from the fundamentals of Python and gradually move toward more advanced concepts. The goal is to build a strong Python foundation that can later be used for automation, software development, data analysis, AI/ML, and other technical domains.

What is Programming?

Programming is the process of giving instructions to a computer so that it can perform a specific task.

A computer is extremely powerful at performing calculations and following instructions, but it does not independently understand what we want it to do. We have to provide instructions in a language that the computer can understand.

For example:

If I ask you:

5 + 6

You can immediately answer:

11

But what about a calculation such as:

23453453 × 56456

You might use a calculator or another tool to get the answer.

Computers, however, are extremely good at performing such repetitive and complex calculations. Programming allows us to instruct computers to perform these tasks automatically and efficiently.

In simple terms:

Programming is the process of writing instructions that tell a computer what to do.

Throughout this 100 Days of Python journey, we will start with the fundamentals and gradually build our programming skills.

What is Python?

Python is a high-level, general-purpose programming language known for its simple and readable syntax.

It supports multiple programming paradigms, including:

• Object-Oriented Programming (OOP)

• Procedural Programming

• Functional Programming

Python is also dynamically typed, which means that you generally do not have to explicitly declare the data type of a variable when creating it.

For example:

name = "Sritesh"
age = 23

Python automatically determines the appropriate types for these values.

Python was created by Guido van Rossum. Its development began in the late 1980s, and Python was first released publicly in 1991.

Features of Python

1. Simple and Readable

Python has a clean and relatively easy-to-understand syntax.

For example:

print("Hello, World!")

This simplicity makes Python particularly suitable for beginners.

2. High-Level Language

Python is a high-level programming language, meaning that its syntax is relatively close to human language compared with lower-level programming languages.

This allows developers to focus more on solving problems rather than managing low-level computer operations.

3. Dynamically Typed

Python is dynamically typed.

For example:

name = "Sritesh"
age = 23

We don't have to explicitly write the data type before the variable.

Python determines the type at runtime.

4. Interpreted

Python programs are executed by the Python interpreter.

This makes the development process convenient because we can quickly write, execute, test, and modify our code.

5. Cross-Platform

Python is available on major operating systems such as:

• Windows

• Linux

• macOS

Therefore, Python programs can generally be developed and executed across different platforms with little or no modification.

6. Open Source

Python is open-source software.

Its source code is publicly available, and a large global community contributes to its ecosystem.

7. Large Ecosystem and Library Support

One of Python's biggest strengths is its huge ecosystem of libraries and frameworks.

Some popular Python libraries and frameworks include:

• NumPy

• Pandas

• Matplotlib

• TensorFlow

• PyTorch

• OpenCV

• Selenium

• FastAPI

• Django

These libraries allow developers to build applications without implementing every feature from scratch.

8. Supports Multiple Programming Paradigms

Python supports different programming approaches, including:

• Procedural programming

• Object-oriented programming

• Functional programming

This makes Python flexible enough for many different types of applications.

What is Python Used For?

Python is used in many different areas of technology.

1. Data Analysis

Python is widely used to process, clean, analyze, and understand data.

Popular tools include:

• Pandas

• NumPy

2. Data Visualization

Python can be used to create charts, graphs, and other visual representations of data.

Popular libraries include:

• Matplotlib

• Seaborn

• Plotly

3. Artificial Intelligence and Machine Learning

Python is one of the most popular programming languages for Artificial Intelligence (AI) and Machine Learning (ML).

It is used to build models that can learn patterns from data and make predictions or decisions.

Popular tools include:

• Scikit-learn

• TensorFlow

• PyTorch

• Hugging Face Transformers

4. Web Development

Python can be used to build web applications and backend services.

Popular frameworks include:

• Django

• Flask

• FastAPI

5. Automation and Scripting

Python is excellent for automating repetitive tasks.

For example, Python can be used to:

• Rename files

• Process large numbers of files

• Automate reports

• Interact with APIs

• Automate system tasks

• Perform DevOps-related tasks

This is one of the areas where Python becomes particularly useful for practical engineering work.

6. Computer Vision

Python is widely used for image and video processing.

Libraries such as OpenCV can be used for tasks including:

• Image processing

• Object detection

• Face detection

• Video processing

7. Database Applications

Python can communicate with databases and can be used to:

• Store data

• Retrieve data

• Update records

• Delete records

• Build database-driven applications

8. Scientific and Mathematical Computing

Python is also widely used for scientific computing, numerical analysis, simulations, and complex mathematical operations.

Libraries such as NumPy and SciPy provide powerful tools for these tasks.

Our First Python Program

Let's write our first Python program:

print("Hello World!")

The print() function is used to display information on the screen.

We can also print numbers:

print(7)

We can even perform calculations:

print(5 + 6)

Output:

11

Day 1 Code

Our main.py file contains:

print("Hello World!")
print(7)

Output:

Hello World!
7

Quick Revision

Programming

Programming is the process of writing instructions that tell a computer what to do.

Python

Python is a high-level, general-purpose, dynamically typed programming language.

Python Creator

Python was created by Guido van Rossum.

First Public Release

Python was first publicly released in 1991.

Major Features

• Simple and readable

• High-level

• Dynamically typed

• Interpreted

• Cross-platform

• Open source

• Large ecosystem

• Supports multiple programming paradigms

Major Uses

• Software development

• Automation

• Web development

• Data analysis

• Data visualization

• Artificial Intelligence

• Machine Learning

• Computer vision

• Scientific computing

• Database applications

Key Takeaway

Python is more than just a beginner-friendly programming language.

Its simple syntax combined with a massive ecosystem makes it useful across many areas of technology—from automation and backend development to Data Science, Machine Learning, Artificial Intelligence, and Generative AI.

This is only Day 1.

The real journey starts now.

100 Days. One language. One step at a time.

📂 Day 1 Resources

👉 All notes and code for this day are available in the GitHub repository:

https://github.com/SriteshSuranjan/100-Days-of-Python/tree/main/01-Day01-Introduction-to-Python